1. Field of the Invention
This invention relates to semiconductor wafer fabrication and more particularly to the acquiring of data during a wafer fabrication process performed by wafer fabrication equipment in real-time and detecting fault conditions in the equipment and/or process by modeling the acquired data.
2. Description of the Relevant Art
In a typical semiconductor manufacturing process, semiconductor wafers are moved through a wafer fabrication assembly line. At each location in the assembly line, processing equipment, or processing tools, perform processing operations to modify the wafers such as by adding layers to the wafers of various materials, and modifying the layers to form a completed product. Examples of processing operations are metal deposition, oxide growth, and source/drain implant.
As the wafers are moved through the assembly line, periodic quality checks are performed on the wafers. The quality checks typically include measuring widths of microscopic lines and film thicknesses on the wafer to detect aberrations. Unfortunately, the measurements can often be made only after the material has undergone processing operations subsequent to those which introduce aberrations. Consequently, the checks can be used to prevent adding more cost to an unusable lot of wafers, but not to save the processed lot from being scrapped. A lot is defined as a set of wafers, typically contained within a wafer boat or cassette.
The quality check measurements add cost in the form of process time and equipment expense. Furthermore, an interval of time typically occurs between introduction of the aberration and detection of the aberration. During the interval, more unusable lots of wafers are processed. This problem is widely known and understood in the semiconductor industry, and various attempts have been and are being made to apply what is called "advanced process control" (APC) to address the problem. In particular, "Fault Detection and Classification" (FDC) systems are one type of APC system which attempts to address the problem.
An FDC system directly monitors process equipment parameters in order to detect conditions which may cause aberrations as they occur. Examples of process parameters are temperature, pressure, power, and flow rates of process materials. A process parameter may be defined as a stimuli upon a processing tool necessary to effectuate a processing outcome such as metal deposition, oxide growth, source/drain implant.
FDC systems collect process parameter data and analyze the data for an abnormality, or fault, during the operation of the process equipment. An example of a process fault is a significant drop in temperature from the temperature required to perform the given process operation, e.g., thermal oxidation. Another example of a fault is a spike in a flow rate of a process material, such as helium. If a fault is detected, the system may have various means of reacting, such as notifying an equipment operator or halting the process equipment. An FDC system typically collects data in one of two methods.
The first data collection method is to receive process parameter data which the process equipment provides through a communications port in the process equipment. Most processing equipment includes built in sensors to gather process parameter data for at least some of the process parameters which the equipment uses. Examples of common process equipment communications ports are RS-232 ports and parallel ports. The data, which may also include state information such as a currently running recipe or recipe step, and event information such as notification of a new wafer or new lot of wafers, is commonly communicated via the SECS-II (Semiconductor Equipment Communications Standard II) or Generic Equipment Model (GEM) protocols on the equipment communications port. A recipe may be defined as a set of process "ingredients", i.e., process parameters, "prepared" according to a set of process steps.
This first method of collecting data has some disadvantages. First, a monitoring system which uses sensor data provided by the process equipment is dependent on the equipment control system and therefore does not perform an independent audit. Second, the data collection rate may be inadequate due to the data transfer rate limitations of the communications port and/or the fact that the processor typically included in process equipment must perform other tasks in addition to collecting and providing process parameter data. For example, a Rapid Thermal Process (RTP) may require high data sampling rates for the temperature signals which are beyond those which may be provided via the communications port. Third, the process equipment may not be able to provide process parameter data on a linear time scale, i.e., at a constant sampling rate, thus complicating analysis of the received data.
A second data collection method employs a data acquisition (DAQ) device. According to this second method, sensors, or transducers, are placed inside or outside the processing equipment which measure the process parameters and convert the measurements into electrical signals, typically voltage signals, which are provided to the DAQ device.
An advantage of the second data collection method is that the DAQ device is capable of acquiring process parameter signal data samples at a constant and relatively high sample rate. Furthermore, the DAQ device is capable of acquiring data samples independent of the process equipment itself. Furthermore, the DAQ device is capable of monitoring signals which may not monitored by the equipment itself, such as room temperature, barometric pressure, etc., or process parameter signals which the equipment was not designed to measure. A disadvantage of the second data collection method is the lack of equipment state or event information, such as the current recipe, process step, wafer number, lot number, etc. since there is no connection to the processing equipment processor. Thus, the equipment state or event information can only be inferred.
Thus, an FDC system which incorporates the advantages of both the data collection methods, i.e., a system which both collects state or event information from the process equipment and acquires real-time process parameter data independent from the intelligence, i.e., processor, of the process equipment is desired.
Once the process parameter data is collected, the data may be analyzed by one of several methods. One data analysis method is to employ a model which predicts expected values of the collected data and provides an indication of the relative conformance of a wafer fabrication process being monitored to a desired process. Different modeling methods may be superior for different wafer fabrication processes or process operations. Therefore, a system which is modular with respect to modeling methods, i.e., allows different process models to be employed which best model the particular process being performed, is desired.